In a traditional modular dialog system, the dialog policy aims to decide a dialog action given a dialog state, while assuming the tasks of language understanding and generation are handled by other components. Under such assumptions, a task-oriented dialog policy mostly follow the slot-filling scheme, which can be described by a set of probabilistic rules. Therefore, we hypothesize that dialog policy in this limited sense can be constructed by learning the underlying rules. To this end, we draw upon the recent advances in developing differentiable inductive logical programs (DILP) [1]} that use neural architectures to learn almost rule-based policies.  We present DILOG, an adaptation of DILP to dialog policy learning. Briefly, DILOG discerns a set of logical rules from the examples by using inductive reasoning.Inductive reasoning tries to summarize general principles from special cases. For example, the fact “cars A, B, and C drive on the right side of the road” induces that “all cars drive on the right side of the road.” We introduce DILOG in Section .
We apply DILOG to the SimDial Dataset [2]} (Section ), and MultiWoZ Dataset [3]}
(Section ), showing that on the task of one-shot dialog policy learning and zero-shot domain transfer, DILOG outperforms several other neural baselines.
Finally, Section  concludes this paper.
